Bramsen Blum (screwkevin74)

OBJECTIVE Over the recent years, several small area electrodes have been introduced as tools for preferential stimulation of small cutaneous nerve fibers. However, the performance of the electrodes is highly debated and have not previously been systematically compared. The electrodes have been developed empirically and little is known about the electrical potential they produce in the skin, and how this influences the nerve fiber activation. The objective of the present study was to develop and validate a computational model to compare the preferential stimulation of small fibers for electrodes of different designs. APPROACH A finite element model of the skin was developed and coupled with an Aβ-fiber and an Aδ-fiber multi-compartmental nerve fiber model, to describe the current spread and consequent nerve fiber activation produced by five different surface electrodes; intra-epidermal, planar concentric, pin, planar array, and patch. The model was validated through experimental assessments of the strength-duration relationship, impedance and reaction times. MAIN RESULTS The computational model predicted the intra-epidermal electrode to be the most preferential for small fiber activation. The intra-epidermal electrode was however also found to be the most sensitive to positioning relative to nerve fiber location, which may limit the practical use of the electrode. SIGNIFICANCE The present study highlights the influence of different electrode design features on the current spread and resulting activation of cutaneous nerve fibers. Additionally, the computational model may be used for the optimization of electrode design towards improved preferential stimulation of small fibers. © 2020 IOP Publishing Ltd.In view of the conciseness of a spiral nanoslit and the limited order of the generated vortex, a kind of nanometer spirals named α spirals are proposed to generate the higher order plasmonic vortex. Theoretical analysis provides the basis for the advancement of α spiral. The proposed spiral can generate the plasmonic vortex and the extreme order of the generated vortex depends on the parameter α. The numerical simulations definite the valid region of the plasmonic vortex generated by the α spiral. Discussions about the validity range of the α spiral nanoslit and the influence of the film material are beneficial to generate the high order vortex. This work builds a platform for the generation of the higher order plasmonic vortex using the simple spiral nanostructure and it can extend the potential applications of higher order plasmonic vortices. © 2020 IOP Publishing Ltd.In this work, we explore deep learning based techniques using the information from mean detector response functions as a new method to estimate gamma ray interaction location in monolithic scintillation crystal detectors. Compared with searching based methods, deep learning techniques do not require recording all the MDRF information once the prediction networks are trained, which means the memory cost could be significantly reduced. In addition, the event positioning process using deep learning techniques only requires running through the network once, without the need to do searching in the reference dataset. This could greatly speed up the positioning process for each event. We have designed and trained four different neural networks to estimate the gamma ray interaction location given the MDRF data. We have studied network structures consisting only of fully connected (FC) layers, as well as convolutional neural networks (CNNs). In addition, we tried to use both regression and classification to generate the final prediction of the gamma ray interaction position. We evaluated the estimation accuracy, testing speed and memory cost (numbers of parameters) of different network architectures, and also compared them with the exhaustive search method. Our results indicate that deep learning based estimation methods with a well designed network structure can achieve